{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a3c606c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#加载数据并可视化\n",
    "\n",
    "#建立模型 预测 \n",
    "#准确率评估表现\n",
    "#求边界\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "078b6cec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test1</th>\n",
       "      <th>test2</th>\n",
       "      <th>pass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.051267</td>\n",
       "      <td>0.69956</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.092742</td>\n",
       "      <td>0.68494</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.213710</td>\n",
       "      <td>0.69225</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.375000</td>\n",
       "      <td>0.50219</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.183760</td>\n",
       "      <td>0.93348</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      test1    test2  pass\n",
       "0  0.051267  0.69956     1\n",
       "1 -0.092742  0.68494     1\n",
       "2 -0.213710  0.69225     1\n",
       "3 -0.375000  0.50219     1\n",
       "4  0.183760  0.93348     0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "data  = pd.read_csv('chip_test.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "52a07fa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      False\n",
      "1      False\n",
      "2      False\n",
      "3      False\n",
      "4       True\n",
      "       ...  \n",
      "113     True\n",
      "114     True\n",
      "115     True\n",
      "116     True\n",
      "117     True\n",
      "Name: pass, Length: 118, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "mask = data.loc[:,'pass']==1\n",
    "print(~mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4333034e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "fig1 = plt.figure()\n",
    "passed = plt.scatter(data.loc[:,'test1'][mask],data.loc[:,'test2'][mask])\n",
    "failed = plt.scatter(data.loc[:,'test1'][~mask],data.loc[:,'test2'][~mask])\n",
    "plt.xlabel('test1')\n",
    "plt.ylabel('test2')\n",
    "plt.legend((passed,failed),('passed','failed'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c9fde0ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test1</th>\n",
       "      <th>test2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.051267</td>\n",
       "      <td>0.69956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.092742</td>\n",
       "      <td>0.68494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.213710</td>\n",
       "      <td>0.69225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.375000</td>\n",
       "      <td>0.50219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.183760</td>\n",
       "      <td>0.93348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      test1    test2\n",
       "0  0.051267  0.69956\n",
       "1 -0.092742  0.68494\n",
       "2 -0.213710  0.69225\n",
       "3 -0.375000  0.50219\n",
       "4  0.183760  0.93348"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = data.drop(['pass'],axis = 1)\n",
    "y = data.loc[:,'pass']\n",
    "x1 = data.loc[:,'test1']\n",
    "x2 = data.loc[:,'test2']\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d8ee87c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           x1        x2      x1_2      x2_2     x1_x2\n",
      "0    0.051267  0.699560  0.002628  0.489384  0.035864\n",
      "1   -0.092742  0.684940  0.008601  0.469143 -0.063523\n",
      "2   -0.213710  0.692250  0.045672  0.479210 -0.147941\n",
      "3   -0.375000  0.502190  0.140625  0.252195 -0.188321\n",
      "4    0.183760  0.933480  0.033768  0.871385  0.171536\n",
      "..        ...       ...       ...       ...       ...\n",
      "113 -0.720620  0.538740  0.519293  0.290241 -0.388227\n",
      "114 -0.593890  0.494880  0.352705  0.244906 -0.293904\n",
      "115 -0.484450  0.999270  0.234692  0.998541 -0.484096\n",
      "116 -0.006336  0.999270  0.000040  0.998541 -0.006332\n",
      "117  0.632650 -0.030612  0.400246  0.000937 -0.019367\n",
      "\n",
      "[118 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "x1_2 = x1*x1\n",
    "x2_2 = x2*x2\n",
    "x1_x2 = x1*x2\n",
    "x_new = {'x1':x1,'x2':x2,'x1_2':x1_2,'x2_2':x2_2,'x1_x2':x1_x2}\n",
    "x_new = pd.DataFrame(x_new)\n",
    "print(x_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "308298ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "LR = LogisticRegression()\n",
    "LR.fit(x_new,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "58bb726e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8135593220338984\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "y_predict = LR.predict(x_new)\n",
    "accuracy = accuracy_score(y,y_predict)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6765c890",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      0.051267\n",
      "1     -0.092742\n",
      "2     -0.213710\n",
      "3     -0.375000\n",
      "4      0.183760\n",
      "         ...   \n",
      "113   -0.720620\n",
      "114   -0.593890\n",
      "115   -0.484450\n",
      "116   -0.006336\n",
      "117    0.632650\n",
      "Name: test1, Length: 118, dtype: float64 112   -0.83007\n",
      "86    -0.75518\n",
      "84    -0.74366\n",
      "111   -0.72638\n",
      "113   -0.72062\n",
      "        ...   \n",
      "70     0.89804\n",
      "65     0.92684\n",
      "68     0.93836\n",
      "67     0.96141\n",
      "101    1.07090\n",
      "Name: test1, Length: 118, dtype: float64\n",
      "[1.19946995] 0.3501464610494275 0.6713687123833844 -2.7815298580915693 -2.3904608348076803 -0.9568611386972843\n",
      "112   NaN\n",
      "86    NaN\n",
      "84    NaN\n",
      "111   NaN\n",
      "113   NaN\n",
      "       ..\n",
      "70    NaN\n",
      "65    NaN\n",
      "68    NaN\n",
      "67    NaN\n",
      "101   NaN\n",
      "Name: test1, Length: 118, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda3\\envs\\sklearn\\lib\\site-packages\\pandas\\core\\arraylike.py:358: RuntimeWarning: invalid value encountered in sqrt\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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0y0XmIAsgWSywbdYJCGSzSZC53KQTuMDs9sjBLB9KbJ3oacbJCXCeLN3qsnQb9eS7TOn0jbdj3IuRg4MnhJqrpu4Kfc+S4AEWQNKK07OGM+Him26jnnyXCZ2+iQydTcdsxWnEOtHTSLIdxo2xzuTg8ORvEaPT9ws6M3L/3enxGciSjusgsU70DOBGn0Xg2qyzlGezuKN0+lZoHr+vvLBOrdbx13VSJtSiMoQ1YaWRTOizMNF5Nou7XpPOF3Tmhsoreb7mBHdf10npuIxvhrIaSBpxewJcpmmsSShoTXeejoiLGEY+cspLRGvEDvRIvCwfOhskFkC84sDY+0COsw+oxpqEIFhriEDs4ciFLZszatqbrv2903IYdJYPnQ0S60T3glNLeJq4NTbgAHB8MEKqos3ibp4roFBZ8/X/qNMzuwM5e9wETqxOdOsD8UKmjL1PI401CQVxAmW04cit8prVCR4QvX8ilblBNgzapMKasLxgo0Y811TTTBCbbeqPiOsz5aWox0UGOidGb9lIPJMsX2sgInKGiKwSkTUiMiXK/hNFpExEloS/bo733EDJ8FEjbi0Pm4rGJkmmywTKeEbdZeUaHF5yMxV8BvAtgIhILnAPMBYYAFwsIgOiHPquqg4Nf92a4LnB4EY6hYB8sIO6pnZjTTPp0mwTT6ALYnNcxsjyxaLi4WcT1ghgTXh9c0RkBnAO8JHL53rP6VEjbq9xkIAgZ/RtrGkmHZpt4hl1l5ajqJoQmCHWbi8/nQH8DCBFQGQ+glLg2CjHjRSRpcAm4FequiKBcxGRicBEgJ49ezpQ7CQ5mcI9QB/sVO+AA3OxCKimAl2mzQ3yZV31WKzvskl+9oFIlG31xxR/CPRS1SHAn4FnEzg3tFH1AVUtVtXizp07J13YQEnyg+1GX0Uqs+OD2vyVTtKlOS5egerTyfC+Syf4WQMpBXpEPO5OqJZxkKrujvj5ZRG5V0Q6xXNuRktijQO37uxSuQMOcvNXOkmH5rh4BapPx2a8N8nPGsgCoK+I9BGRPOAi4PnIA0Ski4hI+OcRhMq7PZ5zM1oSnfJu3dmlcgccqIuFCQQ/8r3FrJlbKvgm+VYDUdUqEZkMvArkAg+r6goRmRTefz9wAXC1iFQBFcBFGpo6H/VcX34RPyTRKe/mxTrZO+BM7AA2qfG6T6fJmnlAlp8OKl8nEqrqy8DL9bbdH/HzX4C/xHtuVknwgx3Ei3WmdQCb1Hmd782aUVNjM9GzRBAv1pYc0kTjZZ+ONaOmxgJIlgjqxTqTOoBN+glizTydWADJInaxTp3NW8ksQayZpxMLIMbEKVCT3IwjglozTxcWQIyJk3W4ZiarmSfP1gMxJk7W4WpMXVYDMWnFzz4I63A1rnBguWu/WA3EpA2/c2elyzoiJo2kecp4CyAmbfidaC/TEheaAEjz5a6tCSvgbNjo14LQB2EdrsZRaZ4y3mogAeZ3k03Q+JFozxhXpXnKeAsgTfFx6Vi/m2wS5fba6NYHYTKOG8tde8iasBrj89KxQWiyiZcXk+xs0pfJOE4vd+0xCyCNSWbpWAeH5KXTsFGvJtlZH4TJOGmcMt6asBqTaAeXw0Py0qnJJp1qS8YYZ1gAaUyiHVwOD8lLp2Gj1sFtspaP/aR+syasxiS6JrILQ/LSpcnGspoatwR6KLvP/aR+87UGIiJniMgqEVkjIlOi7L9ERErCX/8VkSER+9aLyDIRWSIiC10pYKJrIqf5kLxUpFNtyaSPwA9lT/OJgKnyrQYiIrnAPcCpQCmwQESeV9WPIg5bB3xLVXeKyFjgAeDYiP1jVHWbqwVNpIMr0RpLhkmX2pJJH4HPgJzmEwFT5WcNZASwRlXXquoBYAZwTuQBqvpfVd0ZfjgPCPatfKI1FmNMowI/OCOLWx2giRqIiJxO6KL9hqquj9j+Q1V9OMXXLgI2RDwupW7tor4rgFciHiswW0QU+KuqPpBieZyRxkPyjAmawA9lz/JWh5g1EBH5PXATMAh4Q0R+ErF7sgOvLVG2aYyyjCEUQG6I2DxKVYcDY4FrRGR0jHMnishCEVm4devWVMucVdyeWW5MUwI/lD3LWx0aq4GcDQxT1SoRmQo8ISKHqeq1RL/4J6oU6BHxuDuwqf5BIjIYeAgYq6rba7er6qbw9y0i8gyhJrE59c8P10weACguLo4aoDJdMqNYbPlWEwRpkX0gi1sdGgsgzVS1CkBVd4nI2cADIvJvIM+B114A9BWRPsBG4CLgu5EHiEhPYBZwmap+ErG9FZCjquXhn08DsmPYQ4KSDQSB77w0WcMGZwRXY53on4rIt2ofqGq1ql4BrAL6p/rC4eA0GXgVWAnMVNUVIjJJRCaFD7sZ6AjcW2+47qHAeyKyFJgPvKSq/0m1TJko2YSMge+8NMb4rrEayIXRNqrq/xOR+5x4cVV9GXi53rb7I36+ErgyynlrgSH1t5uGkg0Ege+8NMb4LmYNRFUrVLVCRN6IsvsxF8tkHJRsipGgdl5ax74xwRGzBiIi+UBLoJOItOfrjvO2QDcPymYckGyKkSB2XlrHvokl0OlOMlhjTVhXAT8nFCwW8XUA2U1oBrlJA6kEgqB1XlrHvonGbiz8EzOAqOrdwN0i8hNV/bOHZTIOC1ogSJZ17Jto7MbCP/GkMvlCRNoAiMj/E5FZIjLc5XIZ04CljDfR2I2Ff+IJIL8Jz7c4ATgdeBRwZBSWMYkIase+8ZfdWPgnngBSWzc8C7hPVZ/DmYmExiTEUsabaOzGwj/xpHPfKCJ/BU4B/igiLbCVDD21dvkH7Jh9O0de+SCt27b3uzi+ypT+HOOcII4YDJSSmaH1ScpKQ1mCT77ZsdQr8QSQCcAZwB3hlCZdgesceXUTlwN7yyje/RoL355B8bir/S6OMYFjNxYxuLxiYpM1CVXdB2wBTghvqgJWp/zKJm7fKD6ZzXQm76On/S6KMcYrTqy17vKKiU0GEBG5hVAa9RvDm5oD/3Tk1U1ccnJzWd/tTAZULGL7l9mx0pkxWa225lC2AdCvaw6JBhGXV0yMpy/jXGAcsBcOplFv48irm7h1OeEymkkNa9622G1MxnOq5uDyionxBJADqqqEF3sKp083Husz4BjW5fSm3epn/S6KMcZtTtUcTr45tEJiJAdXTIwngMwMj8IqFJEfAa8DDzry6iYhX/Qex5FVK9m4dqXfRTHGuMmpmoPLKybGMwqrM/AUoRxY/Qit0XGKI6+erZIcVtf7W5fC2ulsmPMPig77vQcFjc4S1xnjMifXWndxxcR4AsipqnoD8FrtBhG5k7rrk5t4pTCsrmuvfqxsfhRdP38erflfJMf76ThOJK6zAGRME2qvBS7N33BKzCuQiFwtIsuAfiJSEvG1DijxrogZJsXOsT39v0Ovmg2smPuSC4VrWrIrHNaqDUAbd1WgfB2AbF0PY+oZPAGuXQ5Td4W+Byx4QOM1kCeAV4A/AFMitper6g5XS5XJUuwcGzT2SnaU3MG+t+9m1LutPL+LTzVxnWVOzR5W00yQizPG3dLYioRlqrpeVS9W1c8ivhwLHiJyhoisEpE1IjIlyn4Rkenh/SWRWYCbOjewUuwcyy9oxXsdzmNE5QJal63y/C4+1cR1ljk1O1hNM0FOzfvwmG85rUQkl9DCVGOBAcDFIjKg3mFjgb7hr4mEswDHeW4wOTCs7p7yMezRfK5p9tzBbYk0I6Ui1cR1ljk1O6Ta1Jl1XJ4x7hY/kyKOANao6lpVPQDMAM6pd8w5wGMaMo/QUOKucZ4bTA4Mq/ukvDmPVZ/Gt3Pm0Ve+bvry4i4+1Yy4yQYgWws9vVhNM0Euzxh3SzyjsNxSBGyIeFwKHBvHMUVxnguAiEwkVHuhZ8+eqZXYKSkOq+tWWMADu87istzXuL7ZDH5U+UtAPLuLTyVxXTKZU23J0vTTrbCAjVGChWuf0TTsP6ijXfdw81WU7QHmZw1EomzTOI+J59zQRtUHVLVYVYs7d+6cYBGD6brT+/FV80L+XDWeU3M/ZGzO/LRa/2D8sCLen3IS66adxftTTmoyCFhzSPrxdI2ONO0/qMPlGeNu8bMGUgr0iHjcHdgU5zF5cZybsWovuHe+ksPZ++dya/NHePvkCzL2btyaQ9KPp2t0NNZ/kC61kDSZ91GfnwFkAdBXRPoAG4GLgO/WO+Z5YLKIzCDURFWmqptFZGsc52a02makT0ta0/7ps+j14R9g9JN+FysxcTY7eN4cYhzh2Rodadp/0ICLM8bd4lsTlqpWAZOBV4GVwExVXSEik0RkUviwl4G1wBpC+bd+3Ni5Hv8K3ouyPsDhg49nQdGljNj1Msvffa7p5wiKBJodbMlS0yiXM86a2CSUaDc7FBcX68KFC/0uRnLqp0CBUBvp2dPZf8SZbL39GHKopsOvFlHQKg2y7f9pYIxOwx6hWbf12KQ0E1Mj/xvpdkcfVCKySFWL62/3swkrezgxQqSRdt78wRPYfeqdHDX7YuY9dh3HXX2/c2V3S4LNDrZkqYkpqP0H6T4yLA4WQNzm1JrETVxwjzr+TD5YOI5jvpjBJx9ezDeGfyuFQnsgTYctmoAKWv+By2uRB4Wfw3izg4cri/X/3l1sl/Y0e/GnVB74KsGCeixNhy0aE5c0nVmeKAsgbvNwZbG2hR3ZePxvOaxmPQufuCXBgnrM5YVujPFVpowMa4I1YbnNqaaaONt5h512KR+WzOTodQ/y2aqL6NVvaJIF90DQmh2McUqWNNFaDcRtTjbVxLk+QM9L76FCWrDvqR9TXVUV33NHGSJsjElSljTRWgBxmw9NNZ269OCToTfRv3IF8x/+RdMnZEIqCGMIUNLNLGmitXkgGUprapj/l8s5dsfzLDrmTo4+68rYByc4J8OYIKqfdBNCE04TyRZtoos1D8RqIBlKcnIYdtWDrGw+gAHzb2TN0vdjH5wlHX5eC8zdcJawpJveswCSwfJa5NP5ipnslja0fuZydmyJcQGzVBCOsxX5vGdJN71nASTDderSg93jH6VQd/Hlgxeyv2Jvw4OypMOvKU7WGOxuuC4vamOprnZpNcbEWQDJAn2HfpMVI6bRv3IFH91zMTXVdS9sXnb4BfWf1Okag90Nf82r2lgqSTetxpgcCyBZ4uizrmTeEdcyfM87zL//KrSmpu4BcQ4RTkWQ/0mdrjHY2u9f86o2lspyy1ZjTI5NJAwSl5OvHfvdm5n3180c9+UM5j7SnpE/vN2x545HY/+k0f7JvczA63SN4brT+0UdEZSNKei9rI0lm3TT0xpjBiVZtBpIUHgwF0Nychgx8V7mF57JyM8fYO5jv3HsueORyD+p17UVp2sMqdwNZ5p0qI15VsYMm3NlASQoPEq+lpOby9GT/8HCtqcwcu105j3uXXK3RP5JvW5ScGPRqkTXfveCH31Q6bAgmGdlzLAkixZAgsLDuRi5zZox9CdP8mGr0Ry3+k7mPfk7x18jmkT+Sb3uhM6GGoNffVDp8N56VsYMm3PlSx+IiHQA/gX0BtYDE1R1Z71jegCPAV2AGuABVb07vG8q8CNga/jwX6vqy16U3TUeJ19r1jyPgT/9N4unn89xq25j3j++4rjL3L0Lqv1njKdfw4910DN90apE+6Cc5MV7m2qfmSd//wxLsuhXDWQK8Iaq9gXeCD+urwr4par2B44DrhGRARH7/6SqQ8NfwQseiSYn9GEuRl6LfAb+bBaL2pzEcZ/ezdwHf95wdFYsSSZfjLdZJx2aPdJNJg8tDvIIvzoybM6VXwHkHODR8M+PAuPrH6Cqm1X1w/DP5cBKID1uD5PpKPMp+VrzvBYM/dm/md/hbEZu/Dvz7/kBVZUHGj/Jg47AdGj2SDfp0JmdrLQZhpthSRZ9SaYoIrtUtTDi8U5Vbd/I8b2BOcBAVd0dbsL6PrAbWEioprIzxrkTgYkAPXv2PPqzzz5z6LdoRBomJ9SaGuY99DNGbnqMpQXHcsSPZ9KqzcE/UZ3mgbn5P6PLwdbDCAH7/bwcBpwOMjnZYJ8pLxHtSibAumlnsWXjOjoe2oPcZjZzIRmeJ1MUkddFZHmUr3MSfJ7WwNPAz1V1d3jzfcDhwFBgM3BnrPNV9QFVLVbV4s6dOyf52yQoDYEajUIAABebSURBVDvKJCeHkRP/zAcDbmLgvvlsvusktmxcBzRsHjhEowQPCNTvlzZNGh7K5FpdY7WrfXvK2P+3s1hy94UelyrzuRaOVfWUWPtE5EsR6aqqm0WkK7AlxnHNCQWPx1V1VsRzfxlxzIPAi86V3AFp3FF27ITrWfpWH454ezJ7HzyJT89/kttf3VfnrnWTdqK7bGt4coB+Pz87jIMsUwcKNDZxc/nfrqa4ehO7j53mYwkzk199IM8Dl4d/vhx4rv4BIiLA34CVqvp/9fZ1jXh4LhCcdhNIn46yGB3hQ8ZcyBcXPIcidHnqHPrtrpsK/raqCezTvLrPFbDfL5M7jE1DsWpXPTa9woidL/FB98sZeMI4v4uZcfzqA+kIzAR6Ap8DF6rqDhHpBjykqmeKyAnAu8AyQsN4ITxcV0T+Qaj5SgkNA75KVTc39bqeLigV9HQFtR3hkZOamhfU6dDbumk9ZX87j8Oq1vKnqvP5S/V4NHzPMS7nPX6d92+6sC2Qv9+oaW9GHQZcVFjA+1NO8qFExmub1n1Mm0dOZFPzXhx2/Rya57Xwu0hpK1YfiK1ImK3i7Oiv2FvO/D9/j2/tf5PXqo/mF5VXU07LwHe+ZnKHsWlaVeUB1vxxNEWV6yn//tt063Ok30VKa7Yioakrzo7+glZtGH3908w69KecmLOEF/Ju4oRWGwN/Ic7kDmPTtAWP3sCRVSv5ZMT/WvBwkY1py1YJdPRLTg7nXf1bPp4/mvYvX8XDVTeyeNV2dMgNSE5w70EytcPYNG75u89x7Ia/M7/9mYw460q/i5PRgvvfb9yVREf/kSNOJe+a91nZcjjHfjyNJXecFXuZXGN88NmqJfR442o+z+3OwCvv97s4Gc8CSLZKckZs+85dGfSr/zDvG7/iqL3zqbn3eJa+OcObMhvTiK2b1pP35AVU0Yy8y56iZet2fhcp41knuknap8vmkfPMVfSpWc/8Dmcz4Pt/pnXbmAkFvBP0EXDGcTu3bqbsvtPoXL2FTec+Rd+h3/SvMBn4+bNOdOO4wwcdR7fr5zKvyyUUb3+RPf93DCVvP+1voTJswR7TtJ1bN7PzvjPoUr2Zdac95H/wyKLPnwUQk5IW+S05btK9fHLWv/kqpwWD3/4hC//vArZt8iDnWDQZtmCPady2Lzaw874z6Fa9kdUnP8jAUWf7W6As+/zZKCzjiCNHnMr+QfOZ+8+bOLr0H1T+dQTzjriK4RN+TV6LfO8K0sjwZDeSK1rCRv9sWr+KmkfPoWvNdlaf/CCDRp/rd5HSMg9eKqwGYuLXxBog+QWtGPmju9hy2TusaTmE4z69my/+OJxl78yK8YQuiJGPa19BF8eTKwYpYaMfS9X6ac3S92n2yBm01d18dtYTwQgeEDsfXIDyxDnJAkg6SnIxp5RfM8623e5HDGTIDbNZOvqv5GgNg976AYtvO5NN6z52v5wxhiffVvkdx9eLCMoaFEEKZA248Fld8vqTdJt1LjXksH3Ccxw54lQHCuqQdMmD5xALIOnGr066JNp2h5x0EZ1v+JC5fa6h396FdHjkBOb+7ZdU7C13r5wxhic/umdE1MNTSa4YlISNQQlkDTj8Wa2prmbuw9cx9L1JbGzWg2YT36TPgGOcLXOqMmzBqKZYH0i6aexC7uaHNMm23Rb5LRl5+e/5svQKSv/1K0ZueIjNtz/HymNvYtjpl7szk33whAbvRbeXoydXTGU1Pj/WbY8mKIGsAQc/q2U7t7HugUsYWTGPBe1OZ9BVD5PfsrWDhXVQlM9fprIaSLrxq5MuxbbdQ7sfztG/fIYVpz3J/pxWDP/g56z+/bEsm9Mgk7+zwk0o7+0/j/db/JRxOe8d3JXqGutBWbc9sEvVOvRZXf7e81TcfSxH7VvAB/1/TfHPZgQ3eGQZCyDpxq9OOofado86/kx6/noRCwbfStuqHQx683us+P03+WjuKw4WNiyiCUVQimQbf8z7G+fkvOdIcsWgJGwMSiBrIMXP6v59e5h370QGvn4ZlZLHunNmcex3gp1/LdvYTPR0E8c6Hq6+toMzbPdX7GXJs3dxxKoH6MQuVuQNQkdfz1HHf9uZi0Qark2frEAOJ07hs7pm6Xs0f24SvWo28EGn8xn8g7spaNXG5QKbWGw9EDIkgEDGpUrYv28PS569i8M/eYjO7OSTZt9gT/E1DDnlUnKbpdBNN7WQ0Jpj9QlM3ZX885r4JfhZ3benjKWP/5riTU+yU9rx5Zg7GfSt8zwssInGAggZFEAy1P6KvSx94R6KVv6N7voFG6QbmwZcyZBvTyK/oFXiT5hFNZB0pzU1LH7tcbrNnUoXtjG/8Ez6XXYX7Toe6nfRDAELICLSAfgX0JvQkrQTVHVnlOPWA+VANVBV+wvEe359FkDSQ3VVFUtf+wdtFt1D36rV7KAtq7pfwOFjf8ohRX3ifyI/m/tM3NYsfZ+vXrqRow4sZV1Ob746/XaOPPY0v4tlIgQtgNwG7FDVaSIyBWivqjdEOW49UKyq25I5vz4LIOlFa2pYMfclKt+/lyF751JNDiVtvkn+yIkMGDk2vn6SDGvuyySla5bzxfO3ULz7dXbShk/6T2b4udfa2uUBFLQAsgo4UVU3i0hX4G1VbTBkpJEAEtf59VkASV8b165kw6t3M+DL52nLXjZIN0p7n8cRp/6Izt16+128lAWyE9wln3+yhC9f+h3Dd73GAZqzpOgiBlx4C+3ad/K7aCaGoAWQXapaGPF4p6o2WEhCRNYBOwn1hP5VVR9I5Pz6LICkv4q95Sx/7VFafTSDAQeWUa3C8pYjqB7yXQaOucjbxI0OqU1FEjmbvKB5bsat4f5pyX/Z+dodDN/9Jl/RnKVdzueIc26kU7de6R9AM7ym63kAEZHXgS5Rdt0EPBpnAOmmqptE5BDgNeAnqjonkQAiIhOBiQA9e/Y8+rPPfEozbhy3Yc0ySt96iMM3Ps8h7Ag1g3Q8mbYjvku/4lPIyc1t+kkCYNS06LPkiwoLeH/KST6UyDmzFn7GnJefYELl8xyf+xF7NZ+SbhP4xvgb6HhoaD6I2wHU9eCUBX1tQauBJNwEJSJTgT2qeoc1YZlI1VVVLH/3Gao+fJwBu9+nQA7wJR1Zd+ipFA4/n28UnxzoYNJnykuxBhuzbtpZXhfHEVs2rmPe03czbNuL9MjZyibtwCNVp/OMnMJN54+scwF3M4B6UrvLgtF+sQKIX7mwngcuB6aFvzfIZyEirYAcVS0P/3wacGu855vskdusGUPGXAhjLmTP7p0sfPtfNPv4WYZ/8RR5r8xg6yvtWdtxNPlHfZt+I89Kbkiwi4KSUytVZTu38ck7M8j7+FkGVixknCjv61FMO3Axr9YUUxW+3Nz+6qo6F283c3k1lmjSsQCSZWuARPIrgEwDZorIFcDnwIUQarICHlLVM4FDgWdEpLacT6jqfxo735jWbdtTPG4SjJtEedkOSt6ZSc6qlxi07T+0nPMc+95pweJWR3Og90n0OOZsuvU50u8ic93p/aLeJfueiqSeaE1BJ/XJZ9U7/6L5x88xYN8CjpFqNtOZ+UWXccO6YXyuDedx1A8MbgZQx4NTtL6Odt1j1EAycw2QSL4EEFXdDpwcZfsm4Mzwz2uBIYmcb0ykNu06hIIJk9hfsZeSea9QseIlem57l64f/Rc++t/QZMUOI2h+xIn0Gn7qwXZ5L9XeCQe5EzmyKag9uxmx+11az/otLXJKOEaq+IJOfNhlAh2OvYi+Q0fTNSeH6mlvQhyBwc0A6mhwqt/XUZuefsh3YekTDftAMnQNkEg2E91kHa2p4fPVJWxe9CIFG+ZwxL6ltJL9AKzP6cGX7YeT0/oQum15l277PkYKe3g7qsbvET31Xn/P8dcz5dUvOOpACSNzPmKwrCVHlE3agdcYybFnX0G/4WMazMtJpP/BrY5uR/tAGuvrOPlmG4WV6SyAmGgqD3zFpyXvsXPFm7TaPI/DKpbTOhxQdmorSrUze6QlLY44kS7f/D5de/Z1LyOs3yN6Smay9dmbKK1sw37y6CDlHC6baCY1HNBclurhvFc9iLdrhlCihwE5jXb0NxkYPAiWjgWnLM6tZgEECyAmPtV3DuSzsgNs1UJEoCO76SlbaC6hu9hdtKa0xRHsbXsEHNKftj0HcUjvo+jQuVvqgSWOET1OXRCrKg+wad1HbF+3lP0bV1CwrYSivR/RWcoAqNRc1mg3dmlrlByuqPwlFdSdZ5PSSCm/g2WismC0VSxBG4VlTGDllpdyWI5yGF8e3LZfm7GupoidA3+AbF5CYfknHLblBVpufQpWhI7Zq/l80awbZfnd+aptT3I6HEZBp160ObQXhZ27U9jx0KYDTBMjeuo3ydSufw40CCL7K/ayffPnlG35jIrtpVTu2gi7N5O3ZyPtKz6nW/VGekolPYEaFTbkdmd9zaF8SlcKZQ+95Ev654QumIoArcDJfgq/VtdM1sk3Rw94WdDXEYsFEGPqizKqJl+q+EZhDky47uC2mupqNpeuYcunS6n4cjWyYy35ez6nU8WndNnzPnmb6w4fPaC57JRCynMLqWjWjgN5hVTltUGbt0bzWiP5bcjJHUZOZTmCIhq6bAsKLdqis//Je3PXcWp1BS1yK2lBJa2poJXup/zZR1jwttLiq+20ObCV9jXbKGQPRUBkWKnQPLbldGJHfg++bDeK3C4DKOw1mKK+Q+jVuh29YtxlS7vu/OHEQc72U6Tb8NfaoJbBfR2JsiYsY+pzoGmluqqKrZvWsWvzOvZu30hl2Sa0/Aua7d1C3oGd5FfuolX1bgp0Hy21ggI5kHRxa1TYQz77pBXlzdqzJ68zBwoOoaZNV3LbFVHQoYg2h/Sk/aG9aFvYsfFakJfNSlncJJRurAnLpB+/RiM5cKeZ26wZXXr2pUvPvnEdX1V5gL3lZVTsLaNy5X+oXvwE1Xt3oC07UtN/PNLrOHJycvnN8x/xxV7lAM3Zr3nsIZ/95FFU2JL3p5wUNXdQQry8y7YmobRnNRATTOnWweqRjEu86PeQZRMXq4GY9JJuHaweSYdJhwkZPCGr/57pzgKICaZ062D10PhhRekbMExGcWk2lDEpipVHKAvyCxmTLiyAmGA6+eZQn0ck62A1JlAsgJhgGjwh1GHergcgoe9Z3oFuTNBYH4gJLutgNSbQrAZijDEmKRZAjDHGJMUCiDHGmKRYADHGpI+SmaEcWlMLQ99LZvpdoqzmSwARkQ4i8pqIrA5/bx/lmH4isiTia7eI/Dy8b6qIbIzYd6b3v4UxCbKLX2pq09uUbQD06yVl7X30jV81kCnAG6raF3gj/LgOVV2lqkNVdShwNLAPeCbikD/V7lfVlz0ptTHJsotf6hpLb2N84VcAOQd4NPzzo8D4Jo4/GfhUVT9ztVTGuCUIF790rwFZepvA8SuAHKqqmwHC3w9p4viLgCfrbZssIiUi8nC0JrBaIjJRRBaKyMKtW7emVmpjkuX3xS8TakCW3iZwXAsgIvK6iCyP8nVOgs+TB4wD/h2x+T7gcGAosBm4M9b5qvqAqharanHnzp2T+E2McYDfF78g1IBSZeltAse1meiqekqsfSLypYh0VdXNItIV2NLIU40FPlTVgwtUR/4sIg8CLzpRZmNc4/fiSX7XgJxgS8oGjl+pTJ4HLgemhb8/18ixF1Ov+ao2+IQfngvY+pcm2Py++EVZ5/3g9nRi6W0CxZcVCUWkIzAT6Al8DlyoqjtEpBvwkKqeGT6uJbABOExVyyLO/weh5isF1gNXRQSUmGxFQpO1bIVHk4JArUioqtsJjayqv30TcGbE431AxyjHXeZqAY3JNH7XgExGsmy8xmQLa/4xDrNUJsYYY5JiAcQYY0xSLIAYY4xJigUQY4wxSbEAYowxJim+zAPxi4hsBbxMyNgJ2Obh6yXKypcaK19qrHyp8bJ8vVS1QS6orAogXhORhdEm3wSFlS81Vr7UWPlSE4TyWROWMcaYpFgAMcYYkxQLIO56wO8CNMHKlxorX2qsfKnxvXzWB2KMMSYpVgMxxhiTFAsgxhhjkmIBJEUi0kFEXhOR1eHvDdZnF5F+IrIk4mu3iPw8vG+qiGyM2Hdmw1dxt3zh49aLyLJwGRYmer6b5RORHiLyloisFJEVIvKziH2uvH8icoaIrBKRNSIyJcp+EZHp4f0lIjI83nM9Kt8l4XKViMh/RWRIxL6of2uPy3eiiJRF/N1ujvdcj8p3XUTZlotItYh0CO9z9f0TkYdFZIuIRF0oz+/PXh2qal8pfAG3AVPCP08B/tjE8bnAF4Qm5gBMBX7ld/kILczVKdXfz43yAV2B4eGf2wCfAAPcev/Cf6NPgcOAPGBp7etFHHMm8AogwHHAB/Ge61H5jgfah38eW1u+xv7WHpfvRODFZM71onz1jj8beNPD9280MBxYHmO/b5+9+l9WA0ndOcCj4Z8fBcY3cfzJwKeq6tWM+ETL5/T5KT+/qm5W1Q/DP5cDK4Eih8sRaQSwRlXXquoBYEa4nJHOAR7TkHlAoYh0jfNc18unqv9V1Z3hh/MAL9euTeU9CMT7V0+DZbXdpKpzgB2NHOLnZ68OCyCpO1TDy+mGvx/SxPEX0fDDODlcFX3Y6SaiBMqnwGwRWSQiE5M43+3yASAivYFhwAcRm51+/4oILaVcq5SGASvWMfGc60X5Il1B6I61Vqy/tdflGykiS0XkFRE5KsFzvShf7bLaZwBPR2x2+/1rip+fvTpsRcI4iMjrQJcou25K8HnygHHAjRGb7wN+S+hD+VvgTuCHPpRvlKpuEpFDgNdE5OPwnVDKHHz/WhP6R/65qu4Ob075/Yv2UlG21R/vHuuYeM5NVdyvISJjCAWQEyI2u/a3TqB8HxJqxt0T7rd6Fugb57lelK/W2cD7qhpZI3D7/WuKn5+9OiyAxEFVT4m1T0S+FJGuqro5XI3c0shTjQU+VNUvI5774M8i8iDwoh/l09B69KjqFhF5hlB1eA6QyO/nWvlEpDmh4PG4qs6KeO6U378oSoEeEY+7A5viPCYvjnO9KB8iMhh4CBirqttrtzfyt/asfBE3AKjqyyJyr4h0iudcL8oXoUGLgQfvX1P8/OzVYU1YqXseuDz88+XAc40c26AtNXzRrHUuEHXkRQqaLJ+ItBKRNrU/A6dFlCOR38+t8gnwN2Clqv5fvX1uvH8LgL4i0idca7woXM765f5eeETMcUBZuAkunnNdL5+I9ARmAZep6icR2xv7W3tZvi7hvysiMoLQtWh7POd6Ub5wudoB3yLiM+nR+9cUPz97dbnZQ58NX0BH4A1gdfh7h/D2bsDLEce1JPQP0q7e+f8AlgEl4T92V6/LR2jUxtLw1wrgpqbO97h8JxCqipcAS8JfZ7r5/hEa6fIJoVEtN4W3TQImhX8W4J7w/mVAcWPnuvC5a6p8DwE7I96vhU39rT0u3+Tw6y8l1Ml/fJDev/Dj7wMz6p3n+vtH6CZzM1BJqLZxRZA+e5FflsrEGGNMUqwJyxhjTFIsgBhjjEmKBRBjjDFJsQBijDEmKRZAjDHGJMUCiDEuEJFCEflxkuf+PJxCo/bx70Rkg4jsca6ExqTOAogx7igEkgogwM8JzRuq9QKh2c7GBIqlMjHGHdOAw0VkCfAaoRQtE4AWwDOqekt4JvNMQikncgnl8jqU0CTKt0Rkm6qO0VDGVcITt40JDAsgxrhjCjBQVYeKyGnABYRqEQI8LyKjgc7AJlU9C0KpM1S1TER+AYxR1W1+Fd6YeFgTljHuOy38tZhQFtojCWWeXQacIiJ/FJFvqmqZj2U0JmFWAzHGfQL8QVX/2mCHyNGE8hf9QURmq+qtnpfOmCRZDcQYd5QTWn4X4FXgh+H1TBCRIhE5RES6AftU9Z/AHYSWMa1/rjGBZQHEGBdoaP2N90VkOXAq8AQwV0SWAU8RChCDgPnhjvabgP8Nn/4A8IqIvAUgIreJSCnQUkRKRWSqt7+NMdFZNl5jjDFJsRqIMcaYpFgAMcYYkxQLIMYYY5JiAcQYY0xSLIAYY4xJigUQY4wxSbEAYowxJin/H0MnSXTTLJNYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1_new = x1.sort_values()\n",
    "print(x1,x1_new)\n",
    "\n",
    "theta0 = LR.intercept_\n",
    "theta1,theta2,theta3,theta4,theta5 = LR.coef_[0][0],LR.coef_[0][1],LR.coef_[0][2],LR.coef_[0][3],LR.coef_[0][4]\n",
    "a = theta4\n",
    "b = theta5*x1_new+theta2\n",
    "c = theta0+theta1*x1_new+theta3*x1_new*x1_new\n",
    "x2_new_boundary = (-b+np.sqrt(b*b-4*a*c))/(2*a)\n",
    "print(theta0,theta1,theta2,theta3,theta4,theta5)\n",
    "print(x2_new_boundary)\n",
    "\n",
    "fig4 = plt.figure()\n",
    "plt.plot(x1_new,x2_new_boundary)\n",
    "passed = plt.scatter(data.loc[:,'test1'][mask],data.loc[:,'test2'][mask])\n",
    "failed = plt.scatter(data.loc[:,'test1'][~mask],data.loc[:,'test2'][~mask])#将标记取反，会自动改变颜色\n",
    "plt.plot(x1_new,x2_new_boundary)\n",
    "plt.xlabel('test1')\n",
    "plt.ylabel('test2')\n",
    "plt.legend((passed,failed),('passed','failed'))#加标记\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "87acfea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#以函数方式求解边界曲线\n",
    "def f(x):\n",
    "    a = theta4\n",
    "    b = theta5*x+theta2\n",
    "    c = theta0+theta1*x+theta3*x*x\n",
    "    x2_new_boundary1 = (-b+np.sqrt(b*b-4*a*c))/(2*a)\n",
    "    x2_new_boundary2 = (-b-np.sqrt(b*b-4*a*c))/(2*a)\n",
    "    return x2_new_boundary1,x2_new_boundary2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "84a999fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([0.1212617]), array([0.04679448]), array([0.02697935]), array([0.00872189]), array([-0.00830576]), array([-0.00830576]), array([-0.11718731]), array([-0.16040224]), array([-0.18016521]), array([-0.18965258]), array([-0.21671004]), array([-0.22530078]), array([-0.23369452]), array([-0.2499261]), array([-0.28761583]), array([-0.30846849]), array([-0.32171919]), array([-0.32816104]), array([-0.33448523]), array([-0.34069505]), array([-0.35278365]), array([-0.35866807]), array([-0.37013014]), array([-0.42186137]), array([-0.42656594]), array([-0.43119936]), array([-0.43574686]), array([-0.44021779]), array([-0.45318277]), array([-0.47336128]), array([-0.47336128]), array([-0.48465118]), array([-0.48828317]), array([-0.49185073]), array([-0.49185073]), array([-0.51193977]), array([-0.51193977]), array([-0.5181489]), array([-0.52412149]), array([-0.52986615]), array([-0.52986615]), array([-0.53264971]), array([-0.54066023]), array([-0.54572074]), array([-0.55055962]), array([-0.55055962]), array([-0.55518131]), array([-0.56170859]), array([-0.56775529]), array([-0.56775529]), array([-0.58002265]), array([-0.58002265]), array([-0.58588805]), array([-0.59313744]), array([-0.59416537]), array([-0.59514154]), array([-0.59852963]), array([-0.60052615]), array([-0.60280757]), array([-0.60310565]), array([-0.60354216]), array([-0.60379456]), array([-0.60355793]), array([-0.60282483]), array([-0.60105947]), array([-0.60105947]), array([-0.60047489]), array([-0.59135016]), array([-0.59135016]), array([-0.59009769]), array([-0.5887809]), array([-0.58285568]), array([-0.58285568]), array([-0.57390312]), array([-0.56073894]), array([-0.55572236]), array([-0.53219481]), array([-0.52181028]), array([-0.51814159]), array([-0.51814159]), array([-0.50647589]), array([-0.48925514]), array([-0.47987024]), array([-0.46991909]), array([-0.45383618]), array([-0.42349291]), array([-0.40276684]), array([-0.38769565]), array([-0.3714656]), array([-0.353902]), array([-0.34523055]), array([-0.33477608]), array([-0.33477608]), array([-0.32451646]), array([-0.26416907]), array([-0.26416907]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan])] [array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([0.41115349]), array([0.47870382]), array([0.49620931]), array([0.51216114]), array([0.52688317]), array([0.52688317]), array([0.61731967]), array([0.65131207]), array([0.66646378]), array([0.67364552]), array([0.69378609]), array([0.7000712]), array([0.70615931]), array([0.71777963]), array([0.74393719]), array([0.75787296]), array([0.76651241]), array([0.77064862]), array([0.77466719]), array([0.77857137]), array([0.78604871]), array([0.7896275]), array([0.79647831]), array([0.82515323]), array([0.82755217]), array([0.82987596]), array([0.83211783]), array([0.83428312]), array([0.84033121]), array([0.84898157]), array([0.84898157]), array([0.85335458]), array([0.85468094]), array([0.85594286]), array([0.85594286]), array([0.86219812]), array([0.86219812]), array([0.86379599]), array([0.86515732]), array([0.86628671]), array([0.86628671]), array([0.86676464]), array([0.86785827]), array([0.86830752]), array([0.86853594]), array([0.86853594]), array([0.86854596]), array([0.86815595]), array([0.86728536]), array([0.86728536]), array([0.86341226]), array([0.86341226]), array([0.86005458]), array([0.85346939]), array([0.85219128]), array([0.85086182]), array([0.84502699]), array([0.84010621]), array([0.83085948]), array([0.82885193]), array([0.82467718]), array([0.81801268]), array([0.81085516]), array([0.80320517]), array([0.79221728]), array([0.79221728]), array([0.78932708]), array([0.75714603]), array([0.75714603]), array([0.75358794]), array([0.74996551]), array([0.73481778]), array([0.73481778]), array([0.71433305]), array([0.68733509]), array([0.67770724]), array([0.63573465]), array([0.61843322]), array([0.61245891]), array([0.61245891]), array([0.59387632]), array([0.56742904]), array([0.55343288]), array([0.53887047]), array([0.51587067]), array([0.47399923]), array([0.44635628]), array([0.42667382]), array([0.40583252]), array([0.38365766]), array([0.37284469]), array([0.35992047]), array([0.35992047]), array([0.34735121]), array([0.27547567]), array([0.27547567]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan])]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-28-5d1151224fea>:6: RuntimeWarning: invalid value encountered in sqrt\n",
      "  x2_new_boundary1 = (-b+np.sqrt(b*b-4*a*c))/(2*a)\n",
      "<ipython-input-28-5d1151224fea>:7: RuntimeWarning: invalid value encountered in sqrt\n",
      "  x2_new_boundary2 = (-b-np.sqrt(b*b-4*a*c))/(2*a)\n"
     ]
    }
   ],
   "source": [
    "x2_new_boundary1 = []\n",
    "x2_new_boundary2 = []\n",
    "for x in x1_new :\n",
    "    x2_new_boundary1.append(f(x)[0])\n",
    "    x2_new_boundary2.append(f(x)[1])\n",
    "print(x2_new_boundary1,x2_new_boundary2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "59f35b87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure()\n",
    "passed = plt.scatter(data.loc[:,'test1'][mask],data.loc[:,'test2'][mask])\n",
    "failed = plt.scatter(data.loc[:,'test1'][~mask],data.loc[:,'test2'][~mask])\n",
    "plt.plot(x1_new,x2_new_boundary1)\n",
    "plt.plot(x1_new,x2_new_boundary2)\n",
    "plt.xlabel('test1')\n",
    "plt.ylabel('test2')\n",
    "plt.legend((passed,failed),('passed','failed'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0f1aaeda",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-28-5d1151224fea>:6: RuntimeWarning: invalid value encountered in sqrt\n",
      "  x2_new_boundary1 = (-b+np.sqrt(b*b-4*a*c))/(2*a)\n",
      "<ipython-input-28-5d1151224fea>:7: RuntimeWarning: invalid value encountered in sqrt\n",
      "  x2_new_boundary2 = (-b-np.sqrt(b*b-4*a*c))/(2*a)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan]), array([nan])]\n"
     ]
    }
   ],
   "source": [
    "#为了图像合并，要使数据更密集\n",
    "x1_range = [-0.9 + x/10000 for x in range(0,19000)]\n",
    "x1_range = np.array(x1_range)\n",
    "x2_new_boundary1 = []\n",
    "x2_new_boundary2 = []\n",
    "for x in x1_range :\n",
    "    x2_new_boundary1.append(f(x)[0])\n",
    "    x2_new_boundary2.append(f(x)[1])\n",
    "print(x2_new_boundary1,x2_new_boundary2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5b66b576",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig5 = plt.figure()\n",
    "passed = plt.scatter(data.loc[:,'test1'][mask],data.loc[:,'test2'][mask])\n",
    "failed = plt.scatter(data.loc[:,'test1'][~mask],data.loc[:,'test2'][~mask])\n",
    "plt.plot(x1_range,x2_new_boundary1)\n",
    "plt.plot(x1_range,x2_new_boundary2)\n",
    "plt.xlabel('test1')\n",
    "plt.ylabel('test2')\n",
    "plt.legend((passed,failed),('passed','failed'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9451659f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
